Single-cell RNA-seq reveals a concomitant delay in differentiation and cell cycle of aged hematopoietic stem cells
Bibliographic record
Abstract
BACKGROUND: Hematopoietic stem cells (HSCs) are the guarantor of the proper functioning of hematopoiesis due to their incredible diversity of potential. During aging, heterogeneity of HSCs changes, contributing to the deterioration of the immune system. In this study, we revisited mouse HSC compartment and its transcriptional plasticity during aging at unicellular scale. RESULTS: Through the analysis of 15,000 young and aged transcriptomes, we identified 15 groups of HSCs revealing rare and new specific HSC abilities that change with age. The implantation of new trajectories complemented with the analysis of transcription factor activities pointed consecutive states of HSC differentiation that were delayed by aging and explained the bias in differentiation of older HSCs. Moreover, reassigning cell cycle phases for each HSC clearly highlighted an imbalance of the cell cycle regulators of very immature aged HSCs that may contribute to their accumulation in an undifferentiated state. CONCLUSIONS: Our results establish a new reference map of HSC differentiation in young and aged mice and reveal a potential mechanism that delays the differentiation of aged HSCs and could promote the emergence of age-related hematologic diseases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".